library(tidyverse)
library(rtweet)

Search tweets

search_tweets

bts <- search_tweets("#BTS", n = 5000, include_rts = FALSE)

Downloading [=>---------------------------------------]   4%
Downloading [=>---------------------------------------]   6%
Downloading [==>--------------------------------------]   8%
Downloading [===>-------------------------------------]  10%
Downloading [====>------------------------------------]  12%
Downloading [=====>-----------------------------------]  14%
Downloading [======>----------------------------------]  16%
Downloading [======>----------------------------------]  18%
Downloading [=======>---------------------------------]  20%
Downloading [========>--------------------------------]  22%
Downloading [=========>-------------------------------]  24%
Downloading [==========>------------------------------]  26%
Downloading [==========>------------------------------]  28%
Downloading [===========>-----------------------------]  30%
Downloading [============>----------------------------]  32%
Downloading [=============>---------------------------]  34%
Downloading [==============>--------------------------]  36%
Downloading [===============>-------------------------]  38%
Downloading [===============>-------------------------]  40%
Downloading [================>------------------------]  42%
Downloading [=================>-----------------------]  44%
Downloading [==================>----------------------]  46%
Downloading [===================>---------------------]  48%
Downloading [===================>---------------------]  50%
Downloading [====================>--------------------]  52%
Downloading [=====================>-------------------]  54%
Downloading [======================>------------------]  56%
Downloading [=======================>-----------------]  58%
Downloading [========================>----------------]  60%
Downloading [========================>----------------]  62%
Downloading [=========================>---------------]  64%
Downloading [==========================>--------------]  66%
Downloading [===========================>-------------]  68%
Downloading [============================>------------]  70%
Downloading [=============================>-----------]  72%
Downloading [=============================>-----------]  74%
Downloading [==============================>----------]  76%
Downloading [===============================>---------]  78%
Downloading [================================>--------]  80%
Downloading [=================================>-------]  82%
Downloading [=================================>-------]  84%
Downloading [==================================>------]  86%
Downloading [===================================>-----]  88%
Downloading [====================================>----]  90%
Downloading [=====================================>---]  92%
Downloading [======================================>--]  94%
Downloading [======================================>--]  96%
Downloading [=======================================>-]  98%
Downloading [=========================================] 100%
bts_dynamite <- search_tweets("#BTS dynamite", n = 5000, include_rts = FALSE)

Downloading [=>---------------------------------------]   4%
Downloading [=>---------------------------------------]   6%
Downloading [==>--------------------------------------]   8%
Downloading [===>-------------------------------------]  10%
Downloading [====>------------------------------------]  12%
Downloading [=====>-----------------------------------]  14%
Downloading [======>----------------------------------]  16%
Downloading [======>----------------------------------]  18%
Downloading [=======>---------------------------------]  20%
Downloading [========>--------------------------------]  22%
Downloading [=========>-------------------------------]  24%
Downloading [==========>------------------------------]  26%
Downloading [==========>------------------------------]  28%
Downloading [===========>-----------------------------]  30%
Downloading [============>----------------------------]  32%
Downloading [=============>---------------------------]  34%
Downloading [==============>--------------------------]  36%
Downloading [===============>-------------------------]  38%
Downloading [===============>-------------------------]  40%
Downloading [================>------------------------]  42%
Downloading [=================>-----------------------]  44%
Downloading [==================>----------------------]  46%
Downloading [===================>---------------------]  48%
Downloading [===================>---------------------]  50%
Downloading [====================>--------------------]  52%
Downloading [=====================>-------------------]  54%
Downloading [======================>------------------]  56%
Downloading [=======================>-----------------]  58%
Downloading [========================>----------------]  60%
Downloading [========================>----------------]  62%
Downloading [=========================>---------------]  64%
Downloading [==========================>--------------]  66%
Downloading [===========================>-------------]  68%
Downloading [============================>------------]  70%
Downloading [=============================>-----------]  72%
Downloading [=============================>-----------]  74%
Downloading [==============================>----------]  76%
Downloading [===============================>---------]  78%
Downloading [================================>--------]  80%
Downloading [=================================>-------]  82%
Downloading [=================================>-------]  84%
Downloading [==================================>------]  86%
Downloading [===================================>-----]  88%
Downloading [====================================>----]  90%
Downloading [=====================================>---]  92%
Downloading [======================================>--]  94%
Downloading [======================================>--]  96%
Downloading [=======================================>-]  98%
Downloading [=========================================] 100%
bts
bts_dynamite

get friends

@john_little

john_little <-  get_friends("john_little")
john_little
john_little_data <- lookup_users(john_little$user_id)
john_little_data

get followers

jrl_flw <- get_followers("john_little")
jrl_flw_data <- lookup_users(jrl_flw$user_id)
jrl_flw_data 

timelines

rg_tmls <- get_timelines("RhiannonGiddens", n = 3200)
min(created_at) max(created_at)
2015-04-21 13:59:55 2020-10-27 20:49:18
rg_tmls %>% 
  dplyr::filter(created_at > "2016-01-01") %>%  
  dplyr::group_by(screen_name) %>%
  ts_plot("weeks", trim = 1L) +
  ggplot2::geom_point() +
  geom_smooth(se = FALSE, color = "cadetblue") +
  colorblindr::scale_color_OkabeIto() +
  hrbrthemes::theme_ipsum(grid = "Y") +
  ggplot2::theme(
    legend.title = ggplot2::element_blank(),
    legend.position = "bottom", 
    plot.title = ggplot2::element_text(face = "bold")
    ) +
    ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of Twitter statuses",
    subtitle = "Twitter status (tweet) counts aggregated by week from Jan. 2016",
    caption = "Source: Data collected from Twitter's REST API via rtweet"
  )

NA
NA

get_favorites

rg_faves <- get_favorites("RhiannonGiddens", n = 3000)
rg_faves

search users

gullah <- search_users("#gullah", n = 1000)
Searching for users...
Finished collecting users!
gullah
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